Bayesian Estimation and Unit Root Test for Logistic Smooth Transition Autoregressive Process

The paper considers nonlinear logistic smooth transition autoregressive (LSTAR) process and aims to detect the unit root under the null hypothesis of a random walk process against the alternative of a stationary LSTAR process and to estimate the parameters of the process in Bayesian framework using MCMC. The simulation study is carried out for investigating the performance of the Bayes estimators for parameters and Bayesian unit root test and it has been observed that the estimates of parameters of the LSTAR process are close to the true parameter values. It has been observed that the Bayesian unit root test performs well and the power of the test is high even for the boundary cases having root close to unity, at least when the sample size is large. Since the LSTAR models are widely applied for real exchange rate modeling, the theoretical results are illustrated empirically for the real exchange rates of ten OCED countries.

     

A methodological framework to address gaps in the evidence on infrastructure impacts: the case of an Indian railway project evaluation

ABSTRACT

Infrastructure is a key area of public investment and development cooperation, and can be seen as a critical enabler of trade and integration. Stakeholders increasingly demand evidence on the effectiveness of investments in infrastructure such as railways, in part because these investments typically lock in development patterns for decades. In this article we take stock of the main findings, methodological approaches, gaps and caveats of the current literature with a focus on railways. Based on this analysis, we present a methodology for an impact evaluation framework which builds on existing knowledge and addresses some of these shortcomings. Beyond the dearth of empirical evidence on the socio-economic and environmental impacts of infrastructure, we discuss critiques of the currently prevalent methodological toolbox. Using a real-world railway project in India, the Konkan Railway, we exemplify how a rigorous quantitative impact assessment can integrate inter-disciplinary and mixed-methods features to address these issues. Specifically, we apply different quasi-experimental techniques on the level of intermediate and ultimate outcome and impact indicators, using census, survey and satellite data and information from document analyses, interviews and focus group discussions. We draw on insights from economics, sociology, engineering and geography in making sense of large infrastructure projects and their impacts.     

The queuing system GI/M/I with finite waiting space

Summary Time dependent solution of the queuing system characterized by a general independent input, exponential service time distribution and a finite waiting space, has been first investigated by using the “phase method”. On finding the waiting room full, the customers then arriving may be turned away or the first customer may wait outside and the input process may be stopped till the customer then being served, completes its service. Steady state solutions of both these problems have been obtained and the difference in the operational behaviour of the two systems has been pointed out. For a 2-Erlang arrival distribution, the queuing parameters have been evaluated for different values ofρ _r andN.     

Context-general and Context-specific Determinants of Online Satisfaction and Loyalty for Commerce and Content Sites

We use the context-general and context-specific factor approach to examine the generalizability of satisfaction and loyalty models across two disparate online contexts—online retailing and content site browsing. Our conceptual models include the moderating effects of user-characteristic Web expertise, besides main effects of Web site factors and Web expertise. Results indicate that satisfaction and loyalty judgments are sensitive to both context-general and context-specific determinants, as well as to some interactions between them. Among context-general determinants, ease of use and customer service are positively related to satisfaction, Web community to loyalty, and Web expertise to both satisfaction and loyalty. Flow, a context-specific determinant, has a significant positive effect on satisfaction alone; security affects loyalty alone; and fulfillment/reliability and information quality are significant predictors of both satisfaction and loyalty. The results show that Web expertise moderates the effect of ease of use on satisfaction. The study contributes to marketing theory and practice by identifying satisfaction and loyalty mechanisms that are potentially generalizable across the two online contexts and providing a guiding framework for simultaneous consideration of context-specific and context-general factors in future research.     

Impact of human resources practices on employee creativity in the hotel industry: The impact of job autonomy

ABSTRACT

This study aims to offer a holistic model for human resource practices, affective commitment, job autonomy, and employee creativity. It examines the factors that create conditions for bringing forth creativity in employees. Data were collected from 440 employees drawn from 35 hotels using a structured self-administered questionnaire. Confirmatory factor analysis and hierarchical regression analysis were used to analyze the data. The findings of the study reveal that affective commitment was acted as a mediator between human resource practices and employee creativity while job autonomy was acting as a moderator between affective commitment and employee creativity. This article contributes to a better understanding of the effect of human resource practices on the commitment level of employees working in the hotel industry and provides evidence that commitment acts as a mediator between the factors of human resources practices and employee creativity.     

Drivers of sustainable new technology diffusion in national markets: The case of electric vehicles

This study investigates the factors that impact the diffusion of sustainable new technologies (SNTs) in nations. The article focuses on four country-level variables—development status, focus on sustainability, innovation focus, and type of government—and their relationship with SNT diffusion. In addition to using regression to assess the independent variables' sufficiency, necessary condition analysis is used to analyze the necessity of the variables for SNT diffusion. Data consisted of a 10-year panel data set containing sales of electric vehicles from 2008 to 2017 across 89 countries. Results suggest a significant correlation between the development status of a country and diffusion levels of SNT. Similar results were found for focus on sustainability and innovation focus variables. Finally, relatively established SNT diffusion is affected by government type. Development status, focus on sustainability, and innovation focus were necessary for the diffusion of SNT in a country. In contrast, the absence of dictatorial regimes was also found to be necessary.